GigaOm Radar for AIOps Solutionsv3.01

1. Summary

The last year proved to be one of explosive growth in AIOps tooling and solutions. Since our 2021 Radar report on AIOps, more vendors have added to the proliferation of AIOps solutions. In some cases, AIOps functionality was achieved by bolting an artificial intelligence and machine learning (AI/ML) engine to existing software, via acquisition or internal development, and marketing it as an AIOps solution. Other vendors built entire platforms around homegrown or acquired AI/ML, jumping into a crowded arena competing with pure AI/ML solutions and platform tools.. The market is still segmented into platform players, in which AIOps is one part of a platform of solutions, and other vendors who concentrate on AI while ingesting data from any source. Innovation continues as new vendors push AIOps to the edge of the enterprise.

The key functionality and evaluation metrics we assess demonstrate the realities of a growing vendor landscape and the need to differentiate vendors for enterprises seeking to take advantage of the power an AIOps tool can bring.

This year we’re distinguishing AIOps solutions that require displacing existing tools from those that can be added to the IT tool box without major disruption. Often this dichotomy divides solutions into domain-agnostic and platform solutions. The domain-agnostic solutions can be added to any environment with minimal interruption of the business, while platforms may require the displacement of several existing monitoring solutions. In smaller organizations, displacing the existing monitoring tools is less of a concern because there has been less chance for siloed and homegrown solutions to take root. Typically, large enterprises already have tools for application performance management (APM), infrastructure monitoring, network monitoring, log management, development, and more, so replacing them can be challenging.

Auto-remediation of issues is a more important criterion than in previous reports. IT organizations have turned to automation to address the needs of operations teams. Whether as a distinct part of the AIOps solution or as a hand-off from AIOps, the ability to remediate problems discovered by the power of AI/ML emphasizes the ability of AIOps to do more without the need for additional staff.

AIOps solutions can return the cost of implementation and deployment quickly by reducing the number of operational personnel (or at least not requiring additional headcount) and delivering responses to incidents more quickly.

In the accompanying GigaOm report, “Key Criteria for Evaluating AIOps Solutions,” we list the major features and evaluation metrics that should be applied when selecting an AIOps solution. In this report, we analyze the leading solutions in the market, weigh the key criteria and evaluation metrics used to assess them, and identify important technologies to consider for the future.